強化使用者和電影評分關係，打造 User profile 之電影推薦系統

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Title:	強化使用者和電影評分關係，打造 User profile 之電影推薦系統
Authors:	馬曼容;Ma, Man-Jung
Contributors:	資訊管理學系
Keywords:	推薦系統;電影;個人化檔案;正面使用者檔案;負面使用者檔案
Date:	2020-01-17
Issue Date:	2020-06-05 17:39:00 (UTC+8)
Publisher:	國立中央大學
Abstract:	推薦系統廣泛應用於電影平台上，最廣泛的推薦方法是通過蒐集多個使用者所提供的觀看信息，以評分(Ranking-based)作為依據，與有相同興趣使用者進行比對，根據相似鄰居對項目給出的評分，計算出使用者之間的偏好相似性。最終，對該使用者尚未評分的項目，進行預測性的推薦，亦對使用者提供觀影上更多且更好的建議。然而，儘管以大數據評分為根基，這樣以評分為基礎的電影推薦系統，卻無法細微觀察單一使用者的觀影喜好，甚至以擁有共同經驗之群體喜好作依據，卻不再以個人的觀看或評級紀錄，作為推薦的主要目的，更忽略電影資訊擁有的獨特性區別。本文希望通過正面/負面使用者檔案的建立，進行多元指標的主題分析，增加一部電影的獨特性特徵，並納入過往被忽略的使用者低分評級紀錄，完成更客製且精準的個人化推薦方法。;Recommendation System is widely used on Movie over-the-top platforms. The most widely recommended method is to collect the viewing information provided by multiple users, and use the ratings as the basis to compare with users in the same interests. Based on the ratings given by similar neighbors to the project, the similarity of preferences between users is calculated. In the end, predictive recommendations are made to items that the user has not yet rated, and more and better suggestions are provided to the user. However, despite the big data score as the foundation, this kind of rating-based recommendation system can not observe the single user′s viewing preferences, even based on the group preferences with common experience, but no longer watched by individuals. Or ratings, as the main purpose of the recommendation, ignore the unique differences in film information. This paper hopes to establish a multi-indicator theme analysis through the establishment of positive and negative user files, increase the unique characteristics of single movie, and incorporate the previously ignored users′ low ratings to complete more customized and accurate personalized recommendation system.
Appears in Collections:	[Graduate Institute of Information Management] Electronic Thesis & Dissertation

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